Video fire hazard smoke detecting method based on color saturation degree and movement mode

Abstract

The invention discloses a method for detecting fire disaster smog by video on the basis of color saturation and motion mode, which comprises the following steps: firstly, extracting a foreground motion block from a video image acquired by a monitoring camera through a difference method; secondly, detecting the color saturation of the foreground motion block, estimating the direction of the motion block, and calculating the cumulant and the main moving direction of the motion block; and thirdly, calculating the color saturation detection percentage of each region, the average cumulant and the main moving direction ratio to form a characteristic vector, and adopting a Bayesian classifier to judge whether the smog is the fire disaster smog. The detection of the color saturation reflects a rough color distribution of the smog and eliminates a large amount of foreground interfering objects with glowing colors. The color detection in a blocking mode further improves the anti-interference performance of a system. The cumulant shows the characteristic of continuous movement nearby a smoldering point of the smog, and has very good anti-interference performance for a non-reciprocating object. The comprehensive utilization of the color saturation detection percentage, the average cumulant and the main movement ratio can greatly reduce the rate of false alarm for the system.

Description

technical field [0001] The invention belongs to the technical field of fire monitoring and monitoring, and specifically realizes the automatic detection of video fire smoke of an early fire by using the color saturation and motion mode of fire smoke. Background technique [0002] In places such as urban high-rise buildings and public places, once a fire occurs, major accidents are prone to occur. In order to avoid such situations, traditional smoke and temperature sensing fire detection systems can be installed in these places, which can detect and extinguish fires. However, there are some difficult problems in the traditional smoke and temperature sensing fire detection technology: the detector must be installed near the fire point, otherwise it will not be able to effectively detect the occurrence of disasters; in addition, long-term exposure to dust and other harsh environments may cause Sensor poisoning fails. If this detection system is applied in a high space (such a...

AI Technical Summary

Problems solved by technology

However, there are some difficult problems in the traditional smoke and temperature sensing fire detection technology: the detector must be installed near the fire point, otherwise it will not be able to effectively detect the occurrence of disasters; in addition, long-term exposure to dust and other harsh environments may cause Sensor poisoning failure

Toreyin et al. (Pattern Recognition Letters, 2006, 27: 49-58) proposed a smoke recognition method based on wavelet transform. According to the characteristics of blurred edges of the background image under smoke occlusion, two layers of wavelet transform are used to transform the high frequency subband To study the fuzzy occlusion features of smoke, the wavelet transform can better extract the point singular features in the image, but it cannot extract the line singular features well, and there are certain limitations

Fujiwara et al. (International Symposium on Communications and Information Technologies, October 26-29, 2004, Sapporo Convention Center, Sapporo, Japan) proposed a smoke detection using fractal coding, but only static image smoke detection

At the Suppression and Detection Research and Application Conference (Suppression and Detection Research and Application Conference, SUPDET 2007, Orlando, Florida, March5-8, 2007) held in Orlando, USA in 2007, Xiong Ziyou et al. proposed a method to extract smoke by using Gaussian mixture model motion area, and then identify the method of smoke by analyzing the irregularity of the outline, but because the smoke edge is not obvious and the influence of noise, this method is not effective in the application of complex monitoring conditions

A smoke inspection method based on pixel level introduced by Chinese patent announcement number CN1325903C can identify smoke by detecting the interval of pixel brightness value of the monitoring image. Although the recognition method based on pixel brightness has a faster recognition speed, due to many environmental objects and Smoke has similar color statistics, so false positives are quite high

Most of the feature extraction methods or products that have been proposed in the existing research on visual smoke detection technology at home and abroad adopt pixel-based research methods, which cannot accurately reflect the motion characteristics of smoke, and there are still problems of missed and false positives. Poor stability and adaptability in complex environments

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